Novel approach may offer faster, more accurate COVID-19 detection


Scientists have developed a new approach for detecting the SARS-CoV-2 virus which may lead to tests that are faster, less expensive, and potentially less prone to erroneous results than existing methods.

Although the work, described in the journal Nano Letters, is still theoretical, the researchers said these detectors could potentially be adapted to detect virtually any virus.

The team from Massachusetts Institute of Technology (MIT) in the US noted that existing tests for the SARS-CoV-2 virus, that causes COVID-19, include rapid tests that detect specific viral proteins, and polymerase chain reaction (PCR) tests that take several hours to process.

However, neither of these tests can quantify the amount of virus present with high accuracy, they said.

Even the gold-standard PCR tests might have false-negative rates of more than 25 per cent, according to the researchers.

The team’s analysis shows the new test could have false negative rates below one per cent.

The test could also be sensitive enough to detect just a few hundred strands of the viral RNA, within just a second, they noted.

The new approach makes use of atomic-scale defects in tiny bits of diamond, known as nitrogen vacancy (NV) centers.

These tiny defects are extremely sensitive to minute perturbations, due to quantum effects taking place in the diamond’s crystal lattice, and are being explored for a wide variety of sensing devices that require high sensitivity.

The new method would involve coating the nanodiamonds containing these NV centers with a material that has been treated to bond only with the specific RNA sequence of the virus.

When the virus RNA is present and bonds to this material, it causes changes in the diamond’s fluorescence that are easily detected with a laser-based optical sensor.

The sensor uses only low-cost materials, and the devices could be scaled up to analyse a whole batch of samples at once, the researchers said.

While this work was based on detailed mathematical simulations that proved the system can work in principle, the team is continuing to work on translating that into a working lab-scale device to confirm the predictions.

Their plan is first to do a basic proof-of-principle lab test, and then to work on ways to optimise the system to make it work on real virus diagnosis applications.